The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of Artificial Intelligence (AI) and learning algorithms, propelling vehicles into realms of unprecedented autonomy. This paper provides a comprehensive exploration of the evolutionary trajectory of AI within autonomous vehicles, tracing the journey from foundational principles to the most recent advancements. Commencing with a current landscape overview, the paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing ethical considerations and bias in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI/learning algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI and learning algorithms, and automating key tasks at each level. Additionally, the document discusses the variation in software package sizes across different autonomy levels

翻译：自主车辆的问世标志着交通运输进入变革性时代，通过尖端技术重塑了交通领域的格局。这一演进的核心是人工智能与学习算法的整合，推动车辆实现前所未有的自主水平。本文全面探讨了自主车辆中人工智能的演进轨迹，从基础原理到最新进展进行了系统梳理。研究首先概述当前应用现状，深入剖析人工智能在构建车辆自主决策能力中的基础性作用，详细阐述车辆中人工智能驱动的开发周期各环节，并探讨自主车辆人工智能软件开发中的伦理考量与偏见问题。通过统计分析历年人工智能/学习算法的使用情况与类型，揭示汽车行业研究格局的演变趋势。此外，本文重点阐述了参数在优化卡车与轿车算法中的关键作用，使车辆能够持续适应、学习并提升性能。最后，研究概括了不同自主等级，阐明各等级下人工智能与学习算法的差异化应用及关键任务的自动化实现，并探讨了不同自主等级中软件包体量的差异。